Levenberg‐Marquardt backpropagation algorithm for parameter identification of solid oxide fuel cells

Summary Fast and precise identification of several unknown parameters for solid oxide fuel cell (SOFC) models play a critical role in modeling analysis, optimal control, and behavior prediction. However, inherent high‐nonlinear, multi‐variable, and strongly coupled features usually lead to thorny ob...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	International journal of energy research Ročník 45; číslo 12; s. 17903 - 17923
Hlavní autori:	Yang, Bo, Chen, Yijun, Guo, Zhengxun, Wang, Jingbo, Zeng, Chunyuan, Li, Danyang, Shu, Hongchun, Shan, Jieshan, Fu, Ting, Zhang, Xiaoshun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Chichester, UK John Wiley & Sons, Inc 10.10.2021
Predmet:	Accuracy Algorithms artificial neural network Artificial neural networks Back propagation Back propagation networks Cell culture Electrochemistry Extracellular matrix Fuel cells Fuel technology Heuristic methods Identification Identification methods Levenberg‐Marquardt backpropagation Mathematical models Neural networks Optimal control Optimization optimization methods Parameter estimation Parameter identification parameter identification/estimation Parameters solid oxide fuel cell Solid oxide fuel cells Stability
ISSN:	0363-907X, 1099-114X
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Summary Fast and precise identification of several unknown parameters for solid oxide fuel cell (SOFC) models play a critical role in modeling analysis, optimal control, and behavior prediction. However, inherent high‐nonlinear, multi‐variable, and strongly coupled features usually lead to thorny obstacles that hinder conventional methods to identify them with a high speed, high accuracy, and reliable stability. Hence, a Levenberg‐Marquardt backpropagation (LMBP) algorithm‐based parameter identification technique is proposed in this study, which is applied to efficiently train designed artificial neural networks (ANNs) to implement the identification task. Furthermore, two typical models, for example, electrochemical model (ECM) and steady‐state model (SSM), are taken into account to validate the identification performance of the LMBP algorithm under different operation conditions. Simulation results based on MATLAB demonstrate that the LMBP algorithm can extremely improve the accuracy, speed, and stability for estimating these unknown parameters via a comprehensive comparison with four mainstream meta‐heuristic algorithms, that is, artificial ecosystem‐based optimization (AEO), equilibrium optimizer (EO), grey wolf optimization (GWO), and moth‐flame optimization (MFO).
AbstractList	Fast and precise identification of several unknown parameters for solid oxide fuel cell (SOFC) models play a critical role in modeling analysis, optimal control, and behavior prediction. However, inherent high‐nonlinear, multi‐variable, and strongly coupled features usually lead to thorny obstacles that hinder conventional methods to identify them with a high speed, high accuracy, and reliable stability. Hence, a Levenberg‐Marquardt backpropagation (LMBP) algorithm‐based parameter identification technique is proposed in this study, which is applied to efficiently train designed artificial neural networks (ANNs) to implement the identification task. Furthermore, two typical models, for example, electrochemical model (ECM) and steady‐state model (SSM), are taken into account to validate the identification performance of the LMBP algorithm under different operation conditions. Simulation results based on MATLAB demonstrate that the LMBP algorithm can extremely improve the accuracy, speed, and stability for estimating these unknown parameters via a comprehensive comparison with four mainstream meta‐heuristic algorithms, that is, artificial ecosystem‐based optimization (AEO), equilibrium optimizer (EO), grey wolf optimization (GWO), and moth‐flame optimization (MFO). Summary Fast and precise identification of several unknown parameters for solid oxide fuel cell (SOFC) models play a critical role in modeling analysis, optimal control, and behavior prediction. However, inherent high‐nonlinear, multi‐variable, and strongly coupled features usually lead to thorny obstacles that hinder conventional methods to identify them with a high speed, high accuracy, and reliable stability. Hence, a Levenberg‐Marquardt backpropagation (LMBP) algorithm‐based parameter identification technique is proposed in this study, which is applied to efficiently train designed artificial neural networks (ANNs) to implement the identification task. Furthermore, two typical models, for example, electrochemical model (ECM) and steady‐state model (SSM), are taken into account to validate the identification performance of the LMBP algorithm under different operation conditions. Simulation results based on MATLAB demonstrate that the LMBP algorithm can extremely improve the accuracy, speed, and stability for estimating these unknown parameters via a comprehensive comparison with four mainstream meta‐heuristic algorithms, that is, artificial ecosystem‐based optimization (AEO), equilibrium optimizer (EO), grey wolf optimization (GWO), and moth‐flame optimization (MFO).
Author	Li, Danyang Shu, Hongchun Shan, Jieshan Chen, Yijun Wang, Jingbo Yang, Bo Zeng, Chunyuan Fu, Ting Zhang, Xiaoshun Guo, Zhengxun
Author_xml	– sequence: 1 givenname: Bo orcidid: 0000-0002-5453-0707 surname: Yang fullname: Yang, Bo organization: Kunming University of Science and Technology – sequence: 2 givenname: Yijun surname: Chen fullname: Chen, Yijun organization: Kunming University of Science and Technology – sequence: 3 givenname: Zhengxun surname: Guo fullname: Guo, Zhengxun organization: Kunming University of Science and Technology – sequence: 4 givenname: Jingbo orcidid: 0000-0002-6316-2678 surname: Wang fullname: Wang, Jingbo organization: Kunming University of Science and Technology – sequence: 5 givenname: Chunyuan surname: Zeng fullname: Zeng, Chunyuan organization: Kunming University of Science and Technology – sequence: 6 givenname: Danyang surname: Li fullname: Li, Danyang organization: Kunming University of Science and Technology – sequence: 7 givenname: Hongchun surname: Shu fullname: Shu, Hongchun organization: Kunming University of Science and Technology – sequence: 8 givenname: Jieshan orcidid: 0000-0002-5521-1249 surname: Shan fullname: Shan, Jieshan email: jieshanshan82@outlook.com organization: Kunming University of Science and Technology – sequence: 9 givenname: Ting surname: Fu fullname: Fu, Ting organization: Kunming University of Science and Technology – sequence: 10 givenname: Xiaoshun surname: Zhang fullname: Zhang, Xiaoshun organization: Shantou University
BookMark	eNp1kM1OwzAQhC1UJEpBvIIlDhxQih0ncXxEVfmRipAQSL1FG2ddXNK4dVKgNx6BZ-RJSBtOCE57mE8zs3NIepWrkJATzoacsfAC_TBRodojfc6UCjiPpj3SZyIRgWJyekAO63rOWKtx2Sdmgq9Y5ehnXx-fd-BXa_BFQ3PQL0vvljCDxrqKQjlz3jbPC2qcp0vwsMAGPbUFVo01VneYM7R2pS2oe28VatZYUo1lWR-RfQNljcc_d0CersaPo5tgcn99O7qcBDpM27JhwjBGjHksOctT4BApA4kUuZC5kobxQkQ6B6EjAyFnIQhkqVZSRWkuRCQG5LTzbcuv1lg32dytfdVGZmEsWSjjpB1iQM46SntX1x5NtvR2AX6TcZZtR8zQZ9sRWzL4RWrb7H5tPNjyD_68499siZv_bLPxw47-BjQxhYI
CitedBy_id	crossref_primary_10_3389_fenrg_2022_853991 crossref_primary_10_3389_fenrg_2021_824691 crossref_primary_10_1016_j_rser_2025_116000 crossref_primary_10_1016_j_ijoes_2025_101021 crossref_primary_10_1016_j_renene_2025_123995 crossref_primary_10_1007_s11581_025_06599_1 crossref_primary_10_1016_j_radphyschem_2025_112666 crossref_primary_10_1016_j_compchemeng_2025_109322 crossref_primary_10_1016_j_seta_2021_101825 crossref_primary_10_1140_epjp_s13360_024_05883_9 crossref_primary_10_1016_j_ijft_2024_100849 crossref_primary_10_3389_fenrg_2021_835397 crossref_primary_10_3390_en16062806 crossref_primary_10_1016_j_apradiso_2024_111375 crossref_primary_10_3389_fenrg_2022_844690 crossref_primary_10_3390_pr12112504 crossref_primary_10_1109_TPEL_2022_3211568 crossref_primary_10_3390_su14169882 crossref_primary_10_1016_j_biortech_2023_129820 crossref_primary_10_1016_j_engappai_2024_108141 crossref_primary_10_1007_s10462_023_10696_w crossref_primary_10_1016_j_ijhydene_2023_07_027 crossref_primary_10_1016_j_psep_2025_106998 crossref_primary_10_3390_en17164140 crossref_primary_10_3390_agriculture12060780 crossref_primary_10_1016_j_matchemphys_2025_131442 crossref_primary_10_3390_app14177552 crossref_primary_10_1016_j_powtec_2023_118551
Cites_doi	10.1016/j.energy.2019.116811 10.1016/j.est.2020.101314 10.1016/j.jpowsour.2015.04.155 10.1109/TIE.2016.2618755 10.1016/j.enconman.2017.06.020 10.1109/TIE.2007.896480 10.1021/ie100032c 10.1016/j.ijhydene.2013.09.072 10.1016/j.apenergy.2017.08.027 10.1016/j.jclepro.2019.01.150 10.1016/j.enconman.2018.08.082 10.1016/j.applthermaleng.2019.114310 10.1016/j.enconman.2019.112204 10.1186/s41601-021-00181-3 10.1016/j.energy.2009.02.012 10.1109/JESTPE.2019.2901747 10.1016/j.energy.2018.10.038 10.1109/WCICA.2010.5554639 10.1109/TPWRS.2020.2966509 10.1016/j.energy.2018.11.091 10.1016/j.energy.2019.07.100 10.1016/j.enconman.2020.112595 10.1016/j.ijhydene.2018.03.161 10.1016/j.ijepes.2015.07.036 10.1016/j.jpowsour.2014.08.130 10.4028/www.scientific.net/AMR.557-559.2202 10.1016/j.renene.2019.01.072 10.1109/TNN.2010.2045657 10.1016/j.renene.2017.12.047 10.1016/j.jpowsour.2005.03.174 10.1016/j.apenergy.2018.01.098 10.1016/j.renene.2019.12.105 10.1016/j.enconman.2016.12.063 10.1016/j.ijhydene.2014.01.064 10.1109/TPWRS.2020.3024955 10.1002/fuce.201100140 10.1186/s41601-019-0147-z 10.1016/j.energy.2020.117843 10.1016/j.enconman.2017.12.090 10.1016/j.energy.2019.116613 10.1186/s41601-020-0154-0 10.1016/j.enconman.2020.112856 10.1016/j.ijhydene.2015.04.042 10.1016/j.enconman.2018.10.074 10.1016/j.est.2020.102153 10.1016/j.enconman.2016.10.062
ContentType	Journal Article
Copyright	2021 John Wiley & Sons Ltd. 2021 John Wiley & Sons, Ltd.
Copyright_xml	– notice: 2021 John Wiley & Sons Ltd. – notice: 2021 John Wiley & Sons, Ltd.
DBID	AAYXX CITATION 7SP 7ST 7TB 7TN 8FD C1K F1W F28 FR3 H96 KR7 L.G L7M SOI
DOI	10.1002/er.6929
DatabaseName	CrossRef Electronics & Communications Abstracts Environment Abstracts Mechanical & Transportation Engineering Abstracts Oceanic Abstracts Technology Research Database Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Advanced Technologies Database with Aerospace Environment Abstracts
DatabaseTitle	CrossRef Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Technology Research Database Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts Environmental Sciences and Pollution Management Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Oceanic Abstracts ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Environment Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering
DatabaseTitleList	Civil Engineering Abstracts CrossRef
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1099-114X
EndPage	17923
ExternalDocumentID	10_1002_er_6929 ER6929
Genre	article
GrantInformation_xml	– fundername: National Natural Science Foundation of China funderid: 61963020; 51907112 – fundername: Key Program of National Natural Science Foundation of China funderid: 52037003 – fundername: Major Special Project of Yunnan Province of China funderid: 202002AF080001
GroupedDBID	.3N .GA 05W 0R~ 10A 1L6 1OB 1OC 24P 33P 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHHS AAONW AAXRX AAZKR ABCQN ABCUV ABIJN ABJNI ABPVW ACAHQ ACCFJ ACCZN ACGFS ACIWK ACPOU ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADOZA ADXAS ADZMN ADZOD AEEZP AEIMD AENEX AEQDE AEUQT AFBPY AFGKR AFPWT AFRAH AFZJQ AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN AMBMR AMYDB ATUGU AUFTA AZBYB AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CS3 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM F00 G-S G.N GNP GODZA H.T H.X HHY HZ~ H~9 IX1 J0M JPC KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LYRES MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OIG P2P P2W P2X P4D Q.N Q11 QB0 QRW R.K ROL RWI RX1 RYL SUPJJ TN5 UB1 V2E W8V W99 WBKPD WH7 WIH WIK WLBEL WOHZO WQJ WWI WYISQ XG1 XPP XV2 ZZTAW ~02 ~IA ~WT AAYXX ADMLS CITATION EBS O8X WXSBR 7SP 7ST 7TB 7TN 8FD C1K F1W F28 FR3 H96 KR7 L.G L7M SOI
ID	FETCH-LOGICAL-c2899-260e5ee515710b8a1a49fa673b37b97f01d34cba3c4fa2102a3e08c97948b3343
IEDL.DBID	DRFUL
ISICitedReferencesCount	28
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000662240700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0363-907X
IngestDate	Sun Sep 07 02:40:36 EDT 2025 Sat Nov 29 02:51:56 EST 2025 Tue Nov 18 22:39:12 EST 2025 Wed Jan 22 16:28:07 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	12
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c2899-260e5ee515710b8a1a49fa673b37b97f01d34cba3c4fa2102a3e08c97948b3343
Notes	Funding information Key Program of National Natural Science Foundation of China, Grant/Award Number: 52037003; Major Special Project of Yunnan Province of China, Grant/Award Number: 202002AF080001; National Natural Science Foundation of China, Grant/Award Numbers: 61963020, 51907112 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-5521-1249 0000-0002-5453-0707 0000-0002-6316-2678
PQID	2570275636
PQPubID	996365
PageCount	21
ParticipantIDs	proquest_journals_2570275636 crossref_primary_10_1002_er_6929 crossref_citationtrail_10_1002_er_6929 wiley_primary_10_1002_er_6929_ER6929
PublicationCentury	2000
PublicationDate	10 October 2021
PublicationDateYYYYMMDD	2021-10-10
PublicationDate_xml	– month: 10 year: 2021 text: 10 October 2021 day: 10
PublicationDecade	2020
PublicationPlace	Chichester, UK
PublicationPlace_xml	– name: Chichester, UK – name: Bognor Regis
PublicationTitle	International journal of energy research
PublicationYear	2021
Publisher	John Wiley & Sons, Inc
Publisher_xml	– name: John Wiley & Sons, Inc
References	2017; 119 2018; 166 2017; 64 2021; 6 2019; 192 2005; 152 2020; 203 2016; 74 2019; 168 2020; 36 2020; 35 2008; 55 2017; 210 2019; 163 2017; 135 2018; 43 2012; 12 2020; 208 2019; 188 2020; 8 2009; 34 2018; 174 2010; 21 2020; 5 2010; 49 2015; 291 2021; 34 2018; 215 2015; 40 2015; 277 2018; 159 2020; 194 2020; 150 2019; 215 2019; 138 2019; 179 2016; 133 2020; 24 2020; 213 2014 2014; 39 2012; 557 2017; 148 2020; 29 e_1_2_10_23_1 e_1_2_10_46_1 e_1_2_10_21_1 e_1_2_10_44_1 e_1_2_10_42_1 e_1_2_10_40_1 e_1_2_10_2_1 e_1_2_10_18_1 e_1_2_10_6_1 e_1_2_10_16_1 e_1_2_10_39_1 e_1_2_10_8_1 e_1_2_10_14_1 e_1_2_10_37_1 e_1_2_10_13_1 e_1_2_10_34_1 e_1_2_10_11_1 e_1_2_10_32_1 e_1_2_10_30_1 Hagan MT (e_1_2_10_51_1) 2014 Guchhait PK (e_1_2_10_7_1) 2020; 5 e_1_2_10_29_1 e_1_2_10_27_1 e_1_2_10_25_1 e_1_2_10_48_1 e_1_2_10_24_1 e_1_2_10_45_1 e_1_2_10_22_1 e_1_2_10_43_1 e_1_2_10_20_1 e_1_2_10_41_1 e_1_2_10_52_1 e_1_2_10_3_1 e_1_2_10_19_1 e_1_2_10_5_1 e_1_2_10_17_1 e_1_2_10_38_1 Zhang X (e_1_2_10_4_1) 2020; 24 e_1_2_10_15_1 e_1_2_10_36_1 e_1_2_10_12_1 e_1_2_10_35_1 e_1_2_10_10_1 e_1_2_10_33_1 e_1_2_10_31_1 e_1_2_10_50_1 Tummala ASLV (e_1_2_10_9_1) 2020; 5 e_1_2_10_28_1 e_1_2_10_49_1 e_1_2_10_26_1 e_1_2_10_47_1
References_xml	– volume: 291 start-page: 138 year: 2015 end-page: 155 article-title: A parametric model for solid oxide fuel cells based on measurements made on cell materials and components publication-title: J Power Sources – volume: 49 start-page: 7922 issue: 17 year: 2010 end-page: 7950 article-title: Mathematical modeling, steady‐state and dynamic behavior, and control of fuel cells: a review publication-title: Ind. Eng. Chem. Res – volume: 159 start-page: 312 year: 2018 end-page: 326 article-title: Democratic joint operations algorithm for optimal power extraction of PMSG based wind energy conversion system publication-title: Energy Convers Manage – volume: 24 year: 2020 article-title: Adaptive distributed auction‐based algorithm for optimal mileage based AGC dispatch with high participation of renewable energy publication-title: Int J Electr Power Energy Syst – volume: 64 start-page: 1561 issue: 2 year: 2017 end-page: 1571 article-title: Electrochemical modelling and equivalent circuit representation of micro‐photosynthetic power cell publication-title: IEEE Trans Ind Electron – volume: 133 start-page: 427 year: 2016 end-page: 443 article-title: Grouped grey wolf optimizer for maximum power point tracking of doubly‐fed induction generator based wind turbine publication-title: Energy Convers Manage – volume: 277 start-page: 464 year: 2015 end-page: 473 article-title: Solid oxide fuel cell stack temperature estimation with data‐based modeling – Designed experiments and parameter identification publication-title: J Power Sources – volume: 6 start-page: 37 issue: 1 year: 2021 end-page: 54 article-title: A critical review of the integration of renewable energy sources with various technologies publication-title: Prot Control Mod Power Syst – volume: 208 year: 2020 article-title: Comprehensive overview of meta‐heuristic algorithm applications on PV cell parameter identification publication-title: Energy Convers Manage – volume: 36 start-page: 1493 issue: 2 year: 2020 end-page: 1503 article-title: A hybrid framework for topology identification of distribution grid with renewables integration publication-title: IEEE Trans Power Syst – volume: 34 year: 2021 article-title: Solid oxide fuel cell systems fault diagnosis: critical summarization, classification, and perspectives publication-title: J Energy Storage – volume: 39 start-page: 5083 issue: 10 year: 2014 end-page: 5096 article-title: Parameter identification of an SOFC model with an efficient, adaptive differential evolution algorithm publication-title: Int J Hydrogen Energy – volume: 203 year: 2020 article-title: A simplified competitive swarm optimizer for parameter identification of solid oxide fuel cells publication-title: Energy Convers Manage – volume: 188 year: 2019 article-title: Parameter identification of solid oxide fuel cell by chaotic binary shark smell optimization method publication-title: Energy – volume: 74 start-page: 429 year: 2016 end-page: 436 article-title: Nonlinear maximum power point tracking control and modal analysis of DFIG based wind turbine publication-title: Int J Electr Power Energy Syst – volume: 119 start-page: 577 year: 2017 end-page: 589 article-title: Passivity‐based sliding‐mode control design for optimal power extraction of a PMSG based variable speed wind turbine publication-title: Renewable Energy – volume: 5 start-page: 1 issue: 1 year: 2020 end-page: 20 article-title: Multi‐objective energy management in microgrids with hybrid energy sources and battery energy storage systems publication-title: Prot Control Mod Power Syst – volume: 174 start-page: 913 year: 2018 end-page: 921 article-title: Fast and accurate parameter extraction for different types of fuel cells with decomposition and nature‐inspired optimization method publication-title: Energy Convers Manage – volume: 135 start-page: 21 year: 2017 end-page: 34 article-title: Dynamic model with experimental validation of a biogas‐fed SOFC plant publication-title: Energy Convers Manage – volume: 43 start-page: 8991 issue: 18 year: 2018 end-page: 8998 article-title: Personal power using metal‐supported solid oxide fuel cells operated in a camping stove flame publication-title: Int J Hydrogen Energy – volume: 35 start-page: 2516 issue: 4 year: 2020 end-page: 2526 article-title: Optimal mileage based AGC dispatch of a GenCo publication-title: IEEE Trans Power Syst – volume: 148 start-page: 753 year: 2017 end-page: 758 article-title: Microstructure‐modified proton exchange membranes for high‐performance direct methanol fuel cells publication-title: Energy Convers Manage – volume: 5 start-page: 48 issue: 1 year: 2020 end-page: 55 article-title: Multi‐objective optimization strategy for distribution network considering V2G enabled electric vehicles in building integrated energy system publication-title: Prot Control Mod Power Syst – volume: 168 start-page: 235 year: 2019 end-page: 246 article-title: Solid oxide fuel cell hybrid system: a detailed review of an environmentally clean and efficient source of energy publication-title: Energy – volume: 194 year: 2020 article-title: The role of solid oxide fuel cells in future ship energy systems publication-title: Energy – volume: 210 start-page: 711 year: 2017 end-page: 723 article-title: Robust sliding‐mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers publication-title: Appl Energy – volume: 21 start-page: 930 issue: 6 year: 2010 end-page: 937 article-title: Improved computation for Levenberg‐Marquardt training publication-title: IEEE Trans Neural Netw – volume: 179 start-page: 286 year: 2019 end-page: 303 article-title: Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition publication-title: Energy Convers Manage – volume: 39 start-page: 532 issue: 1 year: 2014 end-page: 542 article-title: Parameter identification for solid oxide fuel cells using cooperative barebone particle swarm optimization with hybrid learning publication-title: Int J Hydrogen Energy – volume: 29 year: 2020 article-title: Energy and environmental analysis of a flexible power‐to‐x plant based on reversible solid oxide cells (rSOCs) for an urban district publication-title: J Energy Storage – volume: 213 year: 2020 article-title: A state‐of‐the‐art survey of solid oxide fuel cell parameter identification: modelling, methodology, and perspectives publication-title: Energy Convers Manage – volume: 203 year: 2020 article-title: Economic viability of bitcoin mining using a renewable‐based SOFC power system to supply the electrical power demand publication-title: Energy – volume: 8 start-page: 1375 issue: 2 year: 2020 end-page: 1390 article-title: Impedance modeling and stability analysis of grid‐connected DFIG‐based wind farm with a VSC‐HVDC publication-title: IEEE J Emerg Sel Top Power Electron – start-page: 800 year: 2014 – volume: 55 start-page: 437 issue: 1 year: 2008 end-page: 447 article-title: A new modeling approach of embedded Fuel‐Cell power generators based on artificial neural network publication-title: IEEE Trans Ind Electron – volume: 163 year: 2019 article-title: 3D design and optimization of heat exchanger network for solid oxide fuel cell‐gas turbine in hybrid electric vehicles publication-title: Appl Therm Eng – volume: 152 start-page: 175 year: 2005 end-page: 181 article-title: Clarifying the Butler‐Volmer equation and related approximations for calculating activation losses in solid oxide fuel cell models publication-title: J Power Sources – volume: 40 start-page: 7246 issue: 23 year: 2015 end-page: 7251 article-title: Modeling electrical behavior of solid oxide electrolyzer cells by using artificial neural network publication-title: Int J Hydrogen Energy – volume: 150 start-page: 221 year: 2020 end-page: 233 article-title: Application of co‐evolution RNA genetic algorithm for obtaining optimal parameters of SOFC model publication-title: Renewable Energy – volume: 5 start-page: 260 issue: 3 year: 2020 end-page: 269 article-title: A robust composite wide area control of a DFIG wind energy system for damping inter‐area oscillations publication-title: Prot Control Mod Power Syst – volume: 192 year: 2019 article-title: Thermo‐economic modeling and analysis of an NG‐fueled SOFC‐WGS‐TSA‐PEMFC hybrid energy conversion system for stationary electricity power generation publication-title: Energy – volume: 138 start-page: 458 year: 2019 end-page: 464 article-title: Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms publication-title: Renewable Energy – volume: 5 start-page: 138 issue: 2 year: 2020 end-page: 150 article-title: Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm publication-title: Prot Control Mod Power Syst – volume: 12 start-page: 11 issue: 1 year: 2012 end-page: 23 article-title: Modeling and optimization of anode‐supported solid oxide fuel cells on cell parameters via artificial neural network and genetic algorithm publication-title: Fuel Cells – volume: 557 start-page: 2202 year: 2012 end-page: 2207 article-title: Modeling of DIR‐SOFC based on particle swarm optimization‐wavelet network publication-title: Adv Mater Res – volume: 215 start-page: 1203 year: 2019 end-page: 1222 article-title: Novel bio‐inspired memetic slap swarm algorithm and application to MPPT for PV systems considering partial shading condition publication-title: J Clean Prod – volume: 166 start-page: 451 year: 2018 end-page: 461 article-title: Parameters' optimization of SOFC for steady state and transient simulations publication-title: Energy – volume: 215 start-page: 237 year: 2018 end-page: 289 article-title: Progress in solid oxide fuel cell‐gas turbine hybrid power systems: system design and analysis, transient operation, controls and optimization publication-title: Appl Energy – volume: 34 start-page: 740 issue: 6 year: 2009 end-page: 751 article-title: Static and dynamic modeling of solid oxide fuel cell using genetic programming publication-title: Energy – volume: 5 start-page: 138 issue: 2 year: 2020 ident: e_1_2_10_7_1 article-title: Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm publication-title: Prot Control Mod Power Syst – ident: e_1_2_10_21_1 doi: 10.1016/j.energy.2019.116811 – ident: e_1_2_10_52_1 – ident: e_1_2_10_18_1 doi: 10.1016/j.est.2020.101314 – start-page: 800 volume-title: Neural Network Design year: 2014 ident: e_1_2_10_51_1 – ident: e_1_2_10_45_1 doi: 10.1016/j.jpowsour.2015.04.155 – ident: e_1_2_10_46_1 doi: 10.1109/TIE.2016.2618755 – ident: e_1_2_10_20_1 doi: 10.1016/j.enconman.2017.06.020 – ident: e_1_2_10_48_1 doi: 10.1109/TIE.2007.896480 – volume: 5 start-page: 260 issue: 3 year: 2020 ident: e_1_2_10_9_1 article-title: A robust composite wide area control of a DFIG wind energy system for damping inter‐area oscillations publication-title: Prot Control Mod Power Syst – ident: e_1_2_10_33_1 doi: 10.1021/ie100032c – ident: e_1_2_10_35_1 doi: 10.1016/j.ijhydene.2013.09.072 – ident: e_1_2_10_14_1 doi: 10.1016/j.apenergy.2017.08.027 – ident: e_1_2_10_17_1 doi: 10.1016/j.jclepro.2019.01.150 – ident: e_1_2_10_36_1 doi: 10.1016/j.enconman.2018.08.082 – ident: e_1_2_10_22_1 doi: 10.1016/j.applthermaleng.2019.114310 – ident: e_1_2_10_47_1 doi: 10.1016/j.enconman.2019.112204 – ident: e_1_2_10_10_1 doi: 10.1186/s41601-021-00181-3 – ident: e_1_2_10_34_1 doi: 10.1016/j.energy.2009.02.012 – ident: e_1_2_10_13_1 doi: 10.1109/JESTPE.2019.2901747 – ident: e_1_2_10_31_1 doi: 10.1016/j.energy.2018.10.038 – ident: e_1_2_10_39_1 doi: 10.1109/WCICA.2010.5554639 – ident: e_1_2_10_8_1 doi: 10.1109/TPWRS.2020.2966509 – volume: 24 year: 2020 ident: e_1_2_10_4_1 article-title: Adaptive distributed auction‐based algorithm for optimal mileage based AGC dispatch with high participation of renewable energy publication-title: Int J Electr Power Energy Syst – ident: e_1_2_10_28_1 doi: 10.1016/j.energy.2018.11.091 – ident: e_1_2_10_42_1 doi: 10.1016/j.energy.2019.07.100 – ident: e_1_2_10_3_1 doi: 10.1016/j.enconman.2020.112595 – ident: e_1_2_10_24_1 doi: 10.1016/j.ijhydene.2018.03.161 – ident: e_1_2_10_12_1 doi: 10.1016/j.ijepes.2015.07.036 – ident: e_1_2_10_38_1 doi: 10.1016/j.jpowsour.2014.08.130 – ident: e_1_2_10_40_1 doi: 10.4028/www.scientific.net/AMR.557-559.2202 – ident: e_1_2_10_44_1 doi: 10.1016/j.renene.2019.01.072 – ident: e_1_2_10_50_1 doi: 10.1109/TNN.2010.2045657 – ident: e_1_2_10_15_1 doi: 10.1016/j.renene.2017.12.047 – ident: e_1_2_10_32_1 doi: 10.1016/j.jpowsour.2005.03.174 – ident: e_1_2_10_27_1 doi: 10.1016/j.apenergy.2018.01.098 – ident: e_1_2_10_26_1 doi: 10.1016/j.renene.2019.12.105 – ident: e_1_2_10_19_1 doi: 10.1016/j.enconman.2016.12.063 – ident: e_1_2_10_41_1 doi: 10.1016/j.ijhydene.2014.01.064 – ident: e_1_2_10_5_1 doi: 10.1109/TPWRS.2020.3024955 – ident: e_1_2_10_43_1 doi: 10.1002/fuce.201100140 – ident: e_1_2_10_2_1 doi: 10.1186/s41601-019-0147-z – ident: e_1_2_10_6_1 doi: 10.1016/j.energy.2020.117843 – ident: e_1_2_10_11_1 doi: 10.1016/j.enconman.2017.12.090 – ident: e_1_2_10_25_1 doi: 10.1016/j.energy.2019.116613 – ident: e_1_2_10_23_1 doi: 10.1186/s41601-020-0154-0 – ident: e_1_2_10_30_1 doi: 10.1016/j.enconman.2020.112856 – ident: e_1_2_10_49_1 doi: 10.1016/j.ijhydene.2015.04.042 – ident: e_1_2_10_16_1 doi: 10.1016/j.enconman.2018.10.074 – ident: e_1_2_10_29_1 doi: 10.1016/j.est.2020.102153 – ident: e_1_2_10_37_1 doi: 10.1016/j.enconman.2016.10.062
SSID	ssj0009917
Score	2.441677
Snippet	Summary Fast and precise identification of several unknown parameters for solid oxide fuel cell (SOFC) models play a critical role in modeling analysis,... Fast and precise identification of several unknown parameters for solid oxide fuel cell (SOFC) models play a critical role in modeling analysis, optimal...
SourceID	proquest crossref wiley
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	17903
SubjectTerms	Accuracy Algorithms artificial neural network Artificial neural networks Back propagation Back propagation networks Cell culture Electrochemistry Extracellular matrix Fuel cells Fuel technology Heuristic methods Identification Identification methods Levenberg‐Marquardt backpropagation Mathematical models Neural networks Optimal control Optimization optimization methods Parameter estimation Parameter identification parameter identification/estimation Parameters solid oxide fuel cell Solid oxide fuel cells Stability
Title	Levenberg‐Marquardt backpropagation algorithm for parameter identification of solid oxide fuel cells
URI	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fer.6929 https://www.proquest.com/docview/2570275636
Volume	45
WOSCitedRecordID	wos000662240700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1099-114X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009917 issn: 0363-907X databaseCode: DRFUL dateStart: 19960101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV09T8MwELWgZYCBb0ShIA8VW2gaO0kzImjFUCpUUalbZDs2VLQNpCli5CfwG_kl3CUpLUJISEwZco6s-M7vXXx5R0jN14FrPO5atuTcAsR3LcB12_JtrYEQAEh5UdZswu92m4NBcLvU6ivXh_j64IaRke3XGOBCTusL0VCdnHuA7auk7IDX8hIpX_Xa_c5CcTfI2u1mB5WQAQ7yP2ZxcL0Y-h2KFvxymaVmMNPe-scEt8lmwS3pRe4MO2RFT3bJxpLi4B4xHVRswpquj7f3G5E8o4ekVAr1CDODzSVbKCpG93EyTB_GFDgtRXnwMZbN0GFUFBflZrGh4LrDiMavcIeamR5RPAmY7pN-u3V3eW0VrRYshRmXBVmNdrUGcgOMQzZFQ_DACM9nkvky8I3diBhXUjDFjcAsUTBtN1UA0dyUjHF2QEqTeKIPCVXMNcAZgoYbAVsJGhIQ0FZcR1IYKR23Qs7mbz1UhQ45tsMYhbmCshPqJMQXV4FnzQ2fcumNnybV-bKFRexNQ-zLh6L2zKuQWrZAvw0PWz28HP3N7JisO1jQguUsdpWU0mSmT8iaekmH0-S0cL9PxmjfJw
linkProvider	Wiley-Blackwell
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NTsMwDLZgIAEH_hHjN4eJW1m3pO16RMAEYkwIbdJuVZImMDE26AbiyCPwjDwJdtsxEEJC4tRDnShK7Phz4nwGKAUm9KwvPMdVQjjo8T0H_brrBK4xCAjQSflxWmwiaDZrnU54lWdV0luYjB_i88CNLCPdr8nA6UC6PGENNcmhj859GmYEKpFXgJmT63q7MaHcDdN6u-lNJYaAnezJLDUu502_-6IJwPwKU1M_U1_6zwiXYTFHl-woU4cVmDL9VVj4wjm4BrZBnE2U1fX--nYpk0fSkRFTUt_h0HB7SZeKyd7NIOmObu8ZolpGBOH3lDjDunGeXpSJDSxD5e3GbPCCf5h9Mj1GdwHDdWjXT1vHZ05ebMHRFHM5GNcYzxiEN4g5VE1WpAit9AOueKDCwLqVmAutJNfCSooTJTduTYdozzXFueAbUOgP-mYTmOaeRdQQVrwY8UpYUegDXS1MrKRVquoV4WA87ZHOmcipIEYvyjiUq5FJIpq4IvY1FnzIyDd-iuyM1y3KrW8YUWU-orXnfhFK6Qr91jw6vabP1t_E9mHurHXZiBrnzYttmK9Segslt7g7UBglT2YXZvXzqDtM9nJd_AAhh-MX
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NTsMwDI5gIAQH_hGDATkgboV2SdvliBgViDEhBNJuVZImMLEf6AbiyCPwjDwJdtuxIYSExKmHOlUU2_HnxvlMyH5ohG8D7juu4tyBiO87ENddJ3SNAUAAQSpIsmYTYbNZa7XEVVFViXdhcn6Irx9u6BnZfo0Obh4TezRmDTXpYQDBfZrMcF8E4JQz9evotjGm3BVZv93spBJSwFZ-ZRYHHxVDv8eiMcCchKlZnImW_jPDZbJYoEt6nJvDCpkyvVWyMME5uEZsAzmbsKrr4-39UqZPaCNDqqR-gKnB9pKpisrOXT9tD--7FFAtRYLwLhbO0HZSlBflYn1LwXjbCe2_whtqn02H4lnAYJ3cRqc3J2dO0WzB0ZhzOZDXGN8YgDeAOVRNepILK4OQKRYqEVrXSxjXSjLNrcQ8UTLj1rQAf64pxjjbIKVev2c2CdXMt4AahOcngFeEpyAGupqbREmrVNUvk4PRsse6YCLHhhidOOdQrsYmjXHhyvCtkeBjTr7xU6Qy0ltceN8gxs58SGvPgjLZzzT02_D49BofW38T2yNzV_Uobpw3L7bJfBWrW7C2xa2Q0jB9NjtkVr8M24N0tzDFT9e54pI
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Levenberg%E2%80%90Marquardt+backpropagation+algorithm+for+parameter+identification+of+solid+oxide+fuel+cells&rft.jtitle=International+journal+of+energy+research&rft.au=Yang%2C+Bo&rft.au=Chen%2C+Yijun&rft.au=Guo%2C+Zhengxun&rft.au=Wang%2C+Jingbo&rft.date=2021-10-10&rft.pub=John+Wiley+%26+Sons%2C+Inc&rft.issn=0363-907X&rft.eissn=1099-114X&rft.volume=45&rft.issue=12&rft.spage=17903&rft.epage=17923&rft_id=info:doi/10.1002%2Fer.6929&rft.externalDBID=10.1002%252Fer.6929&rft.externalDocID=ER6929
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0363-907X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0363-907X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0363-907X&client=summon